A Multi-view Approach to Semi-supervised Document Classification with Incremental Naïve Bayes

Many semi-supervised learning algorithms only consider the distribution of words frequency, ignoring the semantic and syntactic information underlying the documents. In this paper, we present a new multi-view approach for semi-supervised document classification by incorporating both semantic and syntactic information. For this purpose, a co-training style algorithm Co-features is proposed, in active querying, we assign a weight to each sample according to its uncertainty factor, the most informative samples are selected and labeled by other "teachers". In contrast to batch training mode, an incremental Naive Bayes update method is realized, which allow more efficient classifier training even with large pool of unlabeled data. Experimental results show that our algorithm works successfully on Reuters-21578 and WebKB, and is superior to Co-testing in the learning efficiency.